from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = Reporting(config_file_path="config.yml")
reporting.run()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 35.0 | 29.427319 |
| daal4py_KNeighborsClassifier | 0.0 | 2.0 | 24.818884 |
| KNeighborsClassifier_kd_tree | 0.0 | 2.0 | 33.711494 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 0.0 | 27.477394 |
| KMeans_tall | 0.0 | 0.0 | 22.950139 |
| daal4py_KMeans_tall | 0.0 | 0.0 | 8.519828 |
| KMeans_short | 0.0 | 0.0 | 2.721255 |
| daal4py_KMeans_short | 0.0 | 0.0 | 1.365602 |
| LogisticRegression | 0.0 | 0.0 | 19.975836 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 4.250008 |
| Ridge | 0.0 | 0.0 | 10.925706 |
| daal4py_Ridge | 0.0 | 0.0 | 2.045437 |
| HistGradientBoostingClassifier | 0.0 | 5.0 | 4.819674 |
| lightgbm | 0.0 | 5.0 | 12.822656 |
| xgboost | 0.0 | 5.0 | 16.501516 |
| catboost | 0.0 | 6.0 | 0.082929 |
| total | 1.0 | 3.0 | 42.503401 |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.294 | 0.000 | 2.724 | 0.000 | 1 | 100 | NaN | NaN | 0.496 | 0.000 | 0.592 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 24.804 | 0.465 | 0.000 | 0.025 | 1 | 100 | 0.950 | 0.736 | 1.700 | 0.012 | 14.594 | 0.292 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.220 | 0.003 | 0.000 | 0.220 | 1 | 100 | 1.000 | 1.000 | 0.091 | 0.000 | 2.419 | 0.035 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.146 | 0.000 | 5.482 | 0.000 | -1 | 5 | NaN | NaN | 0.489 | 0.000 | 0.299 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 35.668 | 0.000 | 0.000 | 0.036 | -1 | 5 | 0.813 | 0.820 | 1.703 | 0.012 | 20.948 | 0.145 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.185 | 0.014 | 0.000 | 0.185 | -1 | 5 | 1.000 | 1.000 | 0.091 | 0.001 | 2.046 | 0.158 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.141 | 0.000 | 5.685 | 0.000 | 1 | 1 | NaN | NaN | 0.488 | 0.000 | 0.288 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 13.630 | 0.039 | 0.000 | 0.014 | 1 | 1 | 0.736 | 0.820 | 1.704 | 0.022 | 7.997 | 0.105 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.205 | 0.001 | 0.000 | 0.205 | 1 | 1 | 1.000 | 1.000 | 0.092 | 0.001 | 2.239 | 0.019 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.142 | 0.000 | 5.620 | 0.000 | -1 | 100 | NaN | NaN | 0.490 | 0.000 | 0.290 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 35.352 | 0.000 | 0.000 | 0.035 | -1 | 100 | 0.950 | 0.936 | 1.758 | 0.008 | 20.104 | 0.087 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.185 | 0.013 | 0.000 | 0.185 | -1 | 100 | 1.000 | 1.000 | 0.092 | 0.000 | 2.023 | 0.141 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.140 | 0.000 | 5.722 | 0.000 | 1 | 5 | NaN | NaN | 0.490 | 0.000 | 0.286 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 24.074 | 0.082 | 0.000 | 0.024 | 1 | 5 | 0.813 | 0.936 | 1.754 | 0.003 | 13.729 | 0.051 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.212 | 0.001 | 0.000 | 0.212 | 1 | 5 | 1.000 | 1.000 | 0.091 | 0.000 | 2.316 | 0.017 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.137 | 0.000 | 5.856 | 0.000 | -1 | 1 | NaN | NaN | 0.489 | 0.000 | 0.279 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 25.759 | 0.280 | 0.000 | 0.026 | -1 | 1 | 0.736 | 0.736 | 1.701 | 0.013 | 15.144 | 0.201 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.182 | 0.014 | 0.000 | 0.182 | -1 | 1 | 1.000 | 1.000 | 0.091 | 0.002 | 1.997 | 0.159 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.056 | 0.000 | 0.287 | 0.000 | 1 | 100 | NaN | NaN | 0.100 | 0.000 | 0.556 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 21.278 | 0.068 | 0.000 | 0.021 | 1 | 100 | 0.988 | 0.976 | 0.255 | 0.002 | 83.445 | 0.603 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.031 | 0.000 | 0.000 | 0.031 | 1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 6.072 | 0.529 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.056 | 0.000 | 0.284 | 0.000 | -1 | 5 | NaN | NaN | 0.100 | 0.000 | 0.562 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 33.511 | 0.000 | 0.000 | 0.034 | -1 | 5 | 0.986 | 0.984 | 0.256 | 0.001 | 130.795 | 0.356 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.036 | 0.001 | 0.000 | 0.036 | -1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 7.203 | 0.672 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.058 | 0.000 | 0.277 | 0.000 | 1 | 1 | NaN | NaN | 0.100 | 0.000 | 0.576 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 10.745 | 0.028 | 0.000 | 0.011 | 1 | 1 | 0.977 | 0.984 | 0.257 | 0.001 | 41.784 | 0.251 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.015 | 0.001 | 0.000 | 0.015 | 1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 2.887 | 0.243 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.059 | 0.000 | 0.272 | 0.000 | -1 | 100 | NaN | NaN | 0.100 | 0.000 | 0.587 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 32.946 | 0.000 | 0.000 | 0.033 | -1 | 100 | 0.988 | 0.982 | 0.302 | 0.001 | 108.930 | 0.348 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.048 | 0.014 | 0.000 | 0.048 | -1 | 100 | 1.000 | 1.000 | 0.005 | 0.001 | 9.426 | 2.918 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.065 | 0.000 | 0.245 | 0.000 | 1 | 5 | NaN | NaN | 0.100 | 0.000 | 0.651 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 21.539 | 0.383 | 0.000 | 0.022 | 1 | 5 | 0.986 | 0.982 | 0.302 | 0.001 | 71.290 | 1.289 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.031 | 0.001 | 0.000 | 0.031 | 1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 5.973 | 0.534 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.055 | 0.000 | 0.289 | 0.000 | -1 | 1 | NaN | NaN | 0.100 | 0.000 | 0.555 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 23.144 | 0.382 | 0.000 | 0.023 | -1 | 1 | 0.977 | 0.976 | 0.255 | 0.001 | 90.774 | 1.554 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.020 | 0.003 | 0.000 | 0.020 | -1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 3.931 | 0.686 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.769 | 0.000 | 0.029 | 0.000 | 1 | 100 | NaN | NaN | 0.693 | 0.000 | 3.994 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.687 | 0.048 | 0.000 | 0.005 | 1 | 100 | 0.978 | 0.976 | 0.189 | 0.004 | 24.852 | 0.546 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 9.248 | 5.376 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.773 | 0.000 | 0.029 | 0.000 | -1 | 5 | NaN | NaN | 0.678 | 0.000 | 4.089 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.803 | 0.004 | 0.000 | 0.001 | -1 | 5 | 0.977 | 0.962 | 0.101 | 0.001 | 7.923 | 0.083 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 9.788 | 4.609 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.754 | 0.000 | 0.029 | 0.000 | 1 | 5 | NaN | NaN | 0.665 | 0.000 | 4.141 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.382 | 0.014 | 0.000 | 0.001 | 1 | 5 | 0.977 | 0.976 | 0.187 | 0.002 | 7.372 | 0.111 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 4.113 | 2.297 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.780 | 0.000 | 0.029 | 0.000 | -1 | 100 | NaN | NaN | 0.670 | 0.000 | 4.152 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.666 | 0.008 | 0.000 | 0.003 | -1 | 100 | 0.978 | 0.962 | 0.103 | 0.002 | 25.977 | 0.401 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 16.049 | 9.087 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.857 | 0.000 | 0.028 | 0.000 | -1 | 1 | NaN | NaN | 0.662 | 0.000 | 4.313 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.433 | 0.004 | 0.000 | 0.000 | -1 | 1 | 0.972 | 0.976 | 0.552 | 0.002 | 0.784 | 0.008 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 1.000 | 1.000 | 0.001 | 0.000 | 2.777 | 1.245 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.871 | 0.000 | 0.028 | 0.000 | 1 | 1 | NaN | NaN | 0.665 | 0.000 | 4.320 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.732 | 0.004 | 0.000 | 0.001 | 1 | 1 | 0.972 | 0.976 | 0.552 | 0.003 | 1.325 | 0.010 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.001 | 0.000 | 1.156 | 0.564 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.741 | 0.000 | 0.022 | 0.000 | 1 | 100 | NaN | NaN | 0.419 | 0.000 | 1.769 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.050 | 0.002 | 0.000 | 0.000 | 1 | 100 | 0.980 | 0.987 | 0.001 | 0.000 | 45.535 | 11.399 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 5.307 | 4.493 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.743 | 0.000 | 0.022 | 0.000 | -1 | 5 | NaN | NaN | 0.428 | 0.000 | 1.735 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.026 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.979 | 0.978 | 0.001 | 0.000 | 35.735 | 12.093 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 20.547 | 17.281 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.731 | 0.000 | 0.022 | 0.000 | 1 | 5 | NaN | NaN | 0.424 | 0.000 | 1.723 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.024 | 0.001 | 0.001 | 0.000 | 1 | 5 | 0.979 | 0.987 | 0.001 | 0.000 | 22.161 | 6.039 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 5.335 | 5.024 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.726 | 0.000 | 0.022 | 0.000 | -1 | 100 | NaN | NaN | 0.453 | 0.000 | 1.604 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.042 | 0.001 | 0.000 | 0.000 | -1 | 100 | 0.980 | 0.978 | 0.001 | 0.000 | 58.488 | 20.091 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 18.596 | 14.627 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.731 | 0.000 | 0.022 | 0.000 | -1 | 1 | NaN | NaN | 0.430 | 0.000 | 1.701 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.024 | 0.001 | 0.001 | 0.000 | -1 | 1 | 0.975 | 0.989 | 0.006 | 0.001 | 3.931 | 0.556 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 17.232 | 15.216 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.729 | 0.000 | 0.022 | 0.000 | 1 | 1 | NaN | NaN | 0.422 | 0.000 | 1.728 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.023 | 0.001 | 0.001 | 0.000 | 1 | 1 | 0.975 | 0.989 | 0.006 | 0.001 | 3.764 | 0.424 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 4.886 | 3.626 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.618 | 0.000 | 0.776 | 0.000 | k-means++ | NaN | 30 | NaN | 0.404 | 0.0 | 1.529 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.000 | 0.380 | 0.000 | k-means++ | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 8.933 | 5.960 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.000 | 0.000 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.869 | 8.431 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.476 | 0.000 | 1.009 | 0.000 | random | NaN | 30 | NaN | 0.379 | 0.0 | 1.257 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.000 | 0.379 | 0.000 | random | 0.000 | 30 | 0.001 | 0.000 | 0.0 | 9.341 | 6.849 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.000 | 0.000 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.341 | 7.806 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.509 | 0.000 | 3.687 | 0.000 | k-means++ | NaN | 30 | NaN | 3.111 | 0.0 | 2.092 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 15.409 | 0.000 | k-means++ | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 6.064 | 3.178 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.002 | 0.014 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 15.034 | 19.038 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 5.992 | 0.000 | 4.005 | 0.000 | random | NaN | 30 | NaN | 2.941 | 0.0 | 2.037 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 15.852 | 0.000 | random | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.493 | 2.769 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.000 | 0.020 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.792 | 7.081 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 20 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.072 | 0.0 | 0.044 | 0.000 | random | NaN | 20 | NaN | 0.030 | 0.0 | 2.425 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.195 | 0.000 | random | 0.003 | 20 | 0.001 | 0.001 | 0.0 | 2.626 | 0.499 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.162 | 5.857 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.218 | 0.0 | 0.015 | 0.000 | k-means++ | NaN | 20 | NaN | 0.084 | 0.0 | 2.614 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.196 | 0.000 | k-means++ | 0.004 | 20 | 0.002 | 0.001 | 0.0 | 2.630 | 0.613 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 10.080 | 7.472 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.189 | 0.0 | 0.849 | 0.000 | random | NaN | 20 | NaN | 0.128 | 0.0 | 1.478 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.0 | 6.681 | 0.000 | random | 0.322 | 20 | 0.313 | 0.001 | 0.0 | 2.120 | 0.442 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.0 | 0.012 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.892 | 4.949 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.575 | 0.0 | 0.278 | 0.000 | k-means++ | NaN | 20 | NaN | 0.308 | 0.0 | 1.864 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.0 | 6.859 | 0.000 | k-means++ | 0.295 | 20 | 0.319 | 0.001 | 0.0 | 2.127 | 0.386 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.0 | 0.012 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.582 | 4.261 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 10.974 | 0.0 | [-0.10751501] | 0.000 | NaN | NaN | NaN | NaN | NaN | 2.026 | 0.0 | 5.418 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [54.57178543] | 0.000 | NaN | NaN | NaN | NaN | 0.531 | 0.000 | 0.0 | 0.893 | 0.496 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.23756355] | 0.000 | NaN | NaN | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.403 | 0.398 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [27] | 0.789 | 0.0 | [-2.70608541] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.813 | 0.0 | 0.970 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [27] | 0.002 | 0.0 | [137.69279781] | 0.000 | NaN | NaN | NaN | NaN | 0.250 | 0.003 | 0.0 | 0.549 | 0.123 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [27] | 0.000 | 0.0 | [25.51065604] | 0.000 | NaN | NaN | NaN | NaN | 1.000 | 0.001 | 0.0 | 0.124 | 0.102 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | deprecated |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.180 | 0.0 | 0.444 | 0.0 | NaN | NaN | NaN | 0.183 | 0.000 | 0.984 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.012 | 0.0 | 6.661 | 0.0 | NaN | NaN | 0.126 | 0.020 | 0.001 | 0.609 | 0.019 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.0 | 1.195 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.664 | 0.695 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.469 | 0.0 | 0.545 | 0.0 | NaN | NaN | NaN | 0.247 | 0.000 | 5.945 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.0 | 5.322 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.000 | 0.676 | 0.561 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.0 | 0.013 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.686 | 0.709 | See | See |
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}